AIMC Topic: Vocalization, Animal

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Bird song comparison using deep learning trained from avian perceptual judgments.

PLoS computational biology
Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, mos...

5G AI-IoT System for Bird Species Monitoring and Song Classification.

Sensors (Basel, Switzerland)
Identification of different species of animals has become an important issue in biology and ecology. Ornithology has made alliances with other disciplines in order to establish a set of methods that play an important role in the birds' protection and...

Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring.

PloS one
Passive acoustic monitoring is an essential tool for studying beaked whale populations. This approach can monitor elusive and pelagic species, but the volume of data it generates has overwhelmed researchers' ability to quantify species occurrence for...

Machine-learning based detection of marine mammal vocalizations in snapping-shrimp dominated ambient noise.

Marine environmental research
Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise o...

Multi-year soundscape recordings and automated call detection reveals varied impact of moonlight on calling activity of neotropical forest katydids.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Night-time light can have profound ecological effects, even when the source is natural moonlight. The impacts of light can, however, vary substantially by taxon, habitat and geographical region. We used a custom machine learning model built with the ...

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data.

Sensors (Basel, Switzerland)
Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential ...

Automatic classification of dog barking using deep learning.

Behavioural processes
Barking and other dog vocalizations have acoustic properties related to emotions, physiological reactions, attitudes, or some particular internal states. In the field of intelligent audio analysis, researchers use methods based on signal processing a...

Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers.

Journal of the Royal Society, Interface
With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominant...

DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.

PloS one
Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we...

Using machine learning to decode animal communication.

Science (New York, N.Y.)
New methods promise transformative insights and conservation benefits.